{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv(\"day.csv\")\n",
    "train.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 16 columns):\n",
      "instant       731 non-null int64\n",
      "dteday        731 non-null object\n",
      "season        731 non-null int64\n",
      "yr            731 non-null int64\n",
      "mnth          731 non-null int64\n",
      "holiday       731 non-null int64\n",
      "weekday       731 non-null int64\n",
      "workingday    731 non-null int64\n",
      "weathersit    731 non-null int64\n",
      "temp          731 non-null float64\n",
      "atemp         731 non-null float64\n",
      "hum           731 non-null float64\n",
      "windspeed     731 non-null float64\n",
      "casual        731 non-null int64\n",
      "registered    731 non-null int64\n",
      "cnt           731 non-null int64\n",
      "dtypes: float64(4), int64(11), object(1)\n",
      "memory usage: 91.5+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(731, 16)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.info()\n",
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>2.496580</td>\n",
       "      <td>0.500684</td>\n",
       "      <td>6.519836</td>\n",
       "      <td>0.028728</td>\n",
       "      <td>2.997264</td>\n",
       "      <td>0.683995</td>\n",
       "      <td>1.395349</td>\n",
       "      <td>0.495385</td>\n",
       "      <td>0.474354</td>\n",
       "      <td>0.627894</td>\n",
       "      <td>0.190486</td>\n",
       "      <td>848.176471</td>\n",
       "      <td>3656.172367</td>\n",
       "      <td>4504.348837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>211.165812</td>\n",
       "      <td>1.110807</td>\n",
       "      <td>0.500342</td>\n",
       "      <td>3.451913</td>\n",
       "      <td>0.167155</td>\n",
       "      <td>2.004787</td>\n",
       "      <td>0.465233</td>\n",
       "      <td>0.544894</td>\n",
       "      <td>0.183051</td>\n",
       "      <td>0.162961</td>\n",
       "      <td>0.142429</td>\n",
       "      <td>0.077498</td>\n",
       "      <td>686.622488</td>\n",
       "      <td>1560.256377</td>\n",
       "      <td>1937.211452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>183.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.337083</td>\n",
       "      <td>0.337842</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.134950</td>\n",
       "      <td>315.500000</td>\n",
       "      <td>2497.000000</td>\n",
       "      <td>3152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.498333</td>\n",
       "      <td>0.486733</td>\n",
       "      <td>0.626667</td>\n",
       "      <td>0.180975</td>\n",
       "      <td>713.000000</td>\n",
       "      <td>3662.000000</td>\n",
       "      <td>4548.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>548.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.655417</td>\n",
       "      <td>0.608602</td>\n",
       "      <td>0.730209</td>\n",
       "      <td>0.233214</td>\n",
       "      <td>1096.000000</td>\n",
       "      <td>4776.500000</td>\n",
       "      <td>5956.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.861667</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>3410.000000</td>\n",
       "      <td>6946.000000</td>\n",
       "      <td>8714.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          instant      season          yr        mnth     holiday     weekday  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean   366.000000    2.496580    0.500684    6.519836    0.028728    2.997264   \n",
       "std    211.165812    1.110807    0.500342    3.451913    0.167155    2.004787   \n",
       "min      1.000000    1.000000    0.000000    1.000000    0.000000    0.000000   \n",
       "25%    183.500000    2.000000    0.000000    4.000000    0.000000    1.000000   \n",
       "50%    366.000000    3.000000    1.000000    7.000000    0.000000    3.000000   \n",
       "75%    548.500000    3.000000    1.000000   10.000000    0.000000    5.000000   \n",
       "max    731.000000    4.000000    1.000000   12.000000    1.000000    6.000000   \n",
       "\n",
       "       workingday  weathersit        temp       atemp         hum   windspeed  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean     0.683995    1.395349    0.495385    0.474354    0.627894    0.190486   \n",
       "std      0.465233    0.544894    0.183051    0.162961    0.142429    0.077498   \n",
       "min      0.000000    1.000000    0.059130    0.079070    0.000000    0.022392   \n",
       "25%      0.000000    1.000000    0.337083    0.337842    0.520000    0.134950   \n",
       "50%      1.000000    1.000000    0.498333    0.486733    0.626667    0.180975   \n",
       "75%      1.000000    2.000000    0.655417    0.608602    0.730209    0.233214   \n",
       "max      1.000000    3.000000    0.861667    0.840896    0.972500    0.507463   \n",
       "\n",
       "            casual   registered          cnt  \n",
       "count   731.000000   731.000000   731.000000  \n",
       "mean    848.176471  3656.172367  4504.348837  \n",
       "std     686.622488  1560.256377  1937.211452  \n",
       "min       2.000000    20.000000    22.000000  \n",
       "25%     315.500000  2497.000000  3152.000000  \n",
       "50%     713.000000  3662.000000  4548.000000  \n",
       "75%    1096.000000  4776.500000  5956.000000  \n",
       "max    3410.000000  6946.000000  8714.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\AI\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train.cnt.values, bins=60,kde=True)\n",
    "plt.xlabel('cnt',fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(train.shape[0]),train[\"cnt\"].values,color='purple')\n",
    "plt.title(\"cnt\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols=train.columns\n",
    "train_corr = train.corr().abs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(16, 10))\n",
    "sns.heatmap(train_corr,annot=True)\n",
    "\n",
    "sns.heatmap(train_corr, mask=train_corr < 1, cbar=False)\n",
    "\n",
    "plt.savefig('zixingche_coor.png' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\AI\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train.season.values, bins=30,kde=True)\n",
    "plt.xlabel('cnt',fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\AI\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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VvPxhIYOyu3CpOqiSgMnumsZVZ+RRVFbNa6t2+11Oh6bQ7wBqI1H+sGg7qeEQ108ZQkpIb5sEz5j+PTjrpN4s3FzChj0H/S6nw1J6dADzPt7Fvooars0fpCttJdAuHtePnMx0Xl5WSFlVnd/ldEgK/ST3xupiFm8t5ZyRfRiRm+l3OSK+Sg2HuDo/j4qaCD+cW+B3OR2SQj+J7TlYzfdfXsmArAwuGtPX73JEkkJedlc+e3Iuf/5oJwvWFvtdToej0E9Szjn+6y+rqKyJcE3+IPUxLhLngtE5jMzN5AevFFBZE/G7nA5FSZKk/vbxLl5fXcy3p55Mrm6AIvIpKaEQP75yAjsPVPGz19f7XU6HotBPQiUVNdz7agGn5GVxy9nD/C5HJCnlD+3FjWcO5rcfbGHFjgN+l9NhJBT6ZjbNzNaZ2UYzu7OR6elm9kdv+iIzG+qNH2pmVWa23Hs80brld073/3U15dV1PHzVKdqtI3IM35s2mt6Z6dzz6iqiUXXKlojjJoqZhYHHgenAWOA6MxvboNmtwH7n3Ajg58BDcdM2OedO9R63t1LdndZ7G/by6vIivnr+CEb16+53OSJJrUdGKndNH82KwjLmLCv0u5wOIZHNyMnARufcZudcLfAiMKNBmxnAs97wHOBC041Zm626rp4fvLKKYX268R/nn+R3OSIdwhdOG8gZQ7J56O9rde5+AhIJ/YHAjrjXhd64Rts45yJAGdDbmzbMzD4ys3fM7NwW1tup/b9/bGJrySEemDGejNSw3+WIdAhmxg+vGEfpoVoefXOD3+UkvURCv7Et9oY7z5pqswsY7Jw7DfgW8LyZ9TjqD5jdZmZLzWzp3r17Eyip89myr5In/rGJGacO4JyR6kxNpDnGD8ziusmDefZfW1lfrC4ajiWR0C8E4u/DlwcUNdXGzFKALKDUOVfjnCsBcM4tAzYBJzf8A865p5xz+c65/JycYN4B6oG/riYtJcTdl47xuxSRDuk7U0eRmZ7CfXMLcLrTVpMSCf0lwEgzG2ZmacBMYG6DNnOBWd7wVcAC55wzsxzvQDBmNhwYCWxundI7jwVri1mwdg/fuHAkud11Tr7IiejVLY1vTz2ZDzaVqCfOYzhu6Hv76O8A5gNrgJeccwVmdr+ZXeE1ewbobWYbie3GOXxa53nASjNbQewA7+3OudLW/kd0ZDWReu7/39UMz+nGrLOG+l2OSId2/eTBjO7XnQf/toaq2nq/y0lKCd0u0Tk3D5jXYNw9ccPVwNWNzPcy8HILa+zUZr+/la0lh3j2lsmkpeicfJGWSAmH+OEV47j2qYU8+e4mvvm5o/YmB55SxkfF5dX8asEGLhrbl8+eHMxjGSKtbcrw3lw6sT9PvLOJogNVfpeTdBT6PvrxvDVEoo4fXNrwWjcRaYm7po/GOfjJa2v9LiXpKPR9snRrKa8sL+K2c4czuHdXv8sR6VTysrvylfOGM3dFEUu36jBiPIW+D+qjjnvnFtA/K4OvXqArb0Xawu3nn0S/Hhn88H9Xq1+eOAp9H/xxyQ4Kisr5v5eMoWtaQsfSRaSZuqalcOf00Xy8s4yXP1S/PIcp9NvZgUO1PDJ/LZOH9eKyif39LkekU5tx6gBOG9yTh+evo0I3WwEU+u3u52+sp6yqjvsuH4f6pBNpW2bGvZePY+/BGh5/e6Pf5SQFhX47WrOrnOcWbuOGKUMYO+CoLohEpA2cOqgnV54+kGfe28K2kkq/y/GdQr+dRKOOH7yyiqwuqXx7qi4YEWlP3582mpSw8aN5a/wuxXcK/XYy58NClm7bz13Tx9Cza5rf5YgESt8eGXztghHMLyjmg437/C7HVwr9drC/spYfz1tD/pBsrjojz+9yRALp1nOGkZfdhfv/uppIfdTvcnyj0G8HD89fR3l1hAc+P55QSAdvRfyQkRrm7kvGsHb3QZ5fvN3vcnyj0G9jH27fz4tLtnPzWUMZ018Hb0X8NG18P84e0ZtH5q9jz8Fqv8vxhUK/DUXqo/zglVXkdk/nmxfp4K2I38yM+2eMp6Yuyo/+FsyDugr9NvT7hdsoKCrnnsvGkZmuK29FksFJOZl85bPDeWV5USAP6ir020hxeTU/e309547swyUT+vldjojE+doFIxjcqyv/9coqquuCdbMVhX4bcM5x158/pi4a5YEZ43XlrUiSyUgN8+AXxrN5XyW/eHOD3+W0K4V+G/jLRztZsHYP3714NEP7dPO7HBFpxLkjc7gmP4+n39vMx4VlfpfTbhT6ray4vJr75haQPySbm3TPW5GkdvelY+ndLY3vzllBbSQY5+4r9FtRNOr43pyV1ESiPHzVRMI6J18kqWV1SeXBL0xg7e6D/OLN9X6X0y4U+q1o9j+38M76vfzXpWMYnpPpdzkikoCLxvZl5qRB/PqdTSzcXOJ3OW1Ood9KVu0s46G/r+VzY/py45lD/C5HRJrhB5eNZWjvbvznH5dTdqjO73LalEK/FRysruPrL35Er25pPHzVRJ2tI9LBdEtP4dGZp7L3YA3fnbOiU99eUaHfQtGo41svrWBbySF+ce1p9OqmHjRFOqKJeT25c/poXl9dzK/f2eR3OW1God9Cv1qwkTdWF3P3JWP4zEm9/S5HRFrg1nOGcfkpA/jp6+t4Z/1ev8tpEwr9Fnjt4138/M31XHnaQG4+e6jf5YhIC5kZD31xAqP6dufrL3zExj0H/S6p1Sn0T9DCzSV848XlnDa4Jz+6coL244t0El3TUnj63/JJDYeYNXsJu8s6V2+cCv0TsGZXOV9+dimDe3dl9qxJZKSG/S5JRFrRoF5d+e3NkzhwqJab/mcxZVWd54wehX4zFRSVccNvFtEtPYXf3TKZbB24FemUxg/M4okvncGmvRV86ZlFHDhU63dJrUKh3wwfbd/PdU8tJCMlxAu3ncmAnl38LklE2tC5I3N44sYzWLvrIDOfWsi+ihq/S2oxhX6CXi/YzY2/WUR2tzReuv0zDFNHaiKBcOGYvjxzUz5bSyq5+ol/sWlvhd8ltYhC/ziiUcejb27gtueWcVJuJn/6ymfIy+7qd1ki0o7OHZnD72+dQnlVHZ9/7J+8vW6P3yWdMIX+Mewqq+Lm3y45clrmS1/5DLk9MvwuS0R8kD+0F3P/zzkM7t2VW367hB/PW9Mhb8Ci0G9ENOp4aekOpv78XRZvKeWBGeP42TWn6CwdkYAb2LMLc24/i5mTBvPku5u5/Ffvs2zbfr/LahbduLWBDzbu40evrWHVznLyh2Tz06tP0Y1QROSILmlhfnzlBC4e15c7X/6YL/76Ay6d0J/vTRvFkN7JnxUKfaCuPsrfV+1m9j+38NH2Awzs2YVHZ57K5RMHEFKf+CLSiPNH5fLWtz/LU+9u5ql3N/P3gt1MG9+PW84exumDeybtBZsJhb6ZTQMeBcLAb5xzP2kwPR34HXAGUAJc65zb6k27C7gVqAe+7pyb32rVt0BNpJ5l2/bzt5W7+Puq3ZRU1jK0d1d+eMU4rp00SLtyROS4uqWn8J8XncwNUwbzzPtbeH7xdv62chcjcjO5bGJ/po3vx6i+3ZPqC+C4oW9mYeBx4CKgEFhiZnOdc6vjmt0K7HfOjTCzmcBDwLVmNhaYCYwDBgBvmtnJzrl2PfpRG4my80AV63aXs7qonI92HGDJ1lKq66J0SQ1z4ZhcvnDaQC4YlastexFpttweGdx1yRi+fuFIXlm+k7nLi3j0rQ384s0N9MlM48zhvZmYl8W4AVmclJNJbvd037ImkS39ycBG59xmADN7EZgBxIf+DOA+b3gO8JjFvtpmAC8652qALWa20Vvev1qn/E+UV9fx8zfWU14Voby6jvKqOsqrIxw4VEtxeTWHu8cOGZzctzszJw3mrJN6c87IPnRN014uEWm5bukp3DBlCDdMGUJxeTXvrN/LBxv3sXhLKX9duetIu7SUEP2zMsjqkkqPjFR6dEmhe3oqI3Iz+fJ5w9u0xkTSbiCwI+51ITClqTbOuYiZlQG9vfELG8w78ISrPQYXhTnLCumRkUr3jBR6dEllYM8MxvTvTl52VwZld2Fk3+6M6tudLmnadSMibatvjwyuyR/ENfmDACitrGV1UTlbSirZUXqIXWXVlFfVcbC6jt3l1RysrqOorCopQr+x3yANbyvTVJtE5sXMbgNu815WmNm6Bk36APuOU2cyatO6b2ibeZpV84nU0NrLu6GV13M7/ZtOuOY2et8TcaTmJHnfE9GhsmMx8Pt/P+GaE7pPayKhXwgMinudBxQ10abQzFKALKA0wXlxzj0FPNVUAWa21DmXn0CtSaUj1q2a24dqbh+q+WiJXJy1BBhpZsPMLI3Ygdm5DdrMBWZ5w1cBC5xzzhs/08zSzWwYMJLYl5mIiPjguFv63j76O4D5xE7ZnO2cKzCz+4Glzrm5wDPAc96B2lJiXwx47V4idtA3Anytvc/cERGRTyR02opzbh4wr8G4e+KGq4Grm5j3QeDBFtQIx9j1k+Q6Yt2quX2o5vahmhuw2F4YEREJAnW4JiISIEkZ+mb2iJmtNbOVZvYXM+vZRLutZvaxmS03s6XtXadXwzQzW2dmG83szkamp5vZH73pi8xsaPtX+al6BpnZ22a2xswKzOwbjbQ538zKvPW63MzuaWxZ7e1477fF/NJb1yvN7HQ/6oyrZ1TcOlxuZuVm9s0GbXxf12Y228z2mNmquHG9zOwNM9vgPWc3Me8sr80GM5vVWJt2rDmpc6OJmu8zs51x7/8lTcx7zJxpFudc0j2AqUCKN/wQ8FAT7bYCfXysMwxsAoYDacAKYGyDNl8FnvCGZwJ/9Hnd9gdO94a7A+sbqfl84K9+fw6a+34DlwCvEbs+5Exgkd81N/is7AaGJNu6Bs4DTgdWxY17GLjTG76zsf+DQC9gs/ec7Q1n+1hzUudGEzXfB3wngc/OMXOmOY+k3NJ3zr3unIt4LxcSO78/GR3posI5Vwsc7qIi3gzgWW94DnCh+dj7knNul3PuQ2/4ILCGNrpK2gczgN+5mIVATzPr73dRnguBTc65bX4X0pBz7l1iZ93Fi//cPgt8vpFZLwbecM6VOuf2A28A09qs0DiN1ZzsudHEek5EIjmTsKQM/QZuIbb11hgHvG5my7yrettbY11UNAzQT3VRARzuosJ33q6m04BFjUz+jJmtMLPXzGxcuxbWtOO934m8H36ZCbzQxLRkXNd9nXO7ILahAOQ20iaZ13cy50ZDd3i7pGY3sRutVdezbz2NmdmbQL9GJt3tnHvVa3M3sfP7/9DEYs52zhWZWS7whpmt9b5N20tLuqjwlZllAi8D33TOlTeY/CGx3RAV3j7GV4hdWOe3473fybqu04ArgLsamZys6zoRybq+kz034v0aeIDYensA+BmxL6x4rbqefdvSd859zjk3vpHH4cCfBVwG3OC8HVuNLKPIe94D/IXYz6D21JwuKrBPd1HhGzNLJRb4f3DO/bnhdOdcuXOuwhueB6SaWZ92LvMoCbzfCXX74YPpwIfOueKGE5J1XQPFh3eNec+N3Qk86dZ3B8mN+FqKnXP1zrko8HQTtbTqek7K3TsWu2nL94ErnHOHmmjTzcy6Hx4mdhBnVWNt21BLuqjwhXc84RlgjXPuv5to0+/wcQczm0zsc1LSflU2WlMi7/dc4N+8s3jOBMoO76Lw2XU0sWsnGde1J/5zOwt4tZE284GpZpbt7ZaY6o3zRQfKjfh64o85faGJWhLJmcS19xHsRB7ARmL7sJZ7j8NnvwwA5nnDw4kdxV4BFBDbLeRHrZcQOwNm0+EagPuJffAAMoA/ef+mxcBwn9ftOcR+Gq6MW7+XALcDt3tt7vDW6QpiB8TOSoLPRKPvd4O6jdgNfzYBHwP5SVB3V2IhnhU3LqnWNbEvpF1AHbGtyluJHXd6C9jgPffy2uYTu3ve4Xlv8T7bG4Gbfa45qXOjiZqf8z6rK4kFef+GNXuvj8qZE33oilwRkQBJyt07IiLSNhT6IiIBotAXEQkQhb6ISIAo9EVEAkShLyISIAp9kRYys3+Y2b/7XYdIIhT6IiIBotAXiWOxm8z82cz2mlmJmT1mZjeZ2ftm9lMz229mW8xsutf+QeBc4DEDDtNZAAABBElEQVQzqzCzx/z9F4gcm0JfxGNmYeCvwDZgKLHua1/0Jk8B1gF9iN1g5BkzM+fc3cB7wB3OuUzn3B3tXrhIMyj0RT4xmVifJ991zlU656qdc+9707Y55552ztUTu6lIf6CvX4WKnCiFvsgnBhEL90gj03YfHnCf9OCY2S5VibQihb7IJ3YAg737HjSHei2UDkOhL/KJxcS6vv2J1+96hpmdncB8xcS67BVJegp9EY+3v/5yYASwnVif59cmMOujwFXemT2/bMMSRVpM/emLiASItvRFRAJEoS8iEiAKfRGRAFHoi4gEiEJfRCRAFPoiIgGi0BcRCRCFvohIgCj0RUQC5P8D8JFBOztV9KwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train.mnth.values, bins=30,kde=True)\n",
    "plt.xlabel('cnt',fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\AI\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train.weathersit.values, bins=16,kde=True)\n",
    "plt.xlabel('cnt',fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\AI\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train.weekday.values, bins=30,kde=True)\n",
    "plt.xlabel('cnt',fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "weathersit属性的不同取值和出现的次数\n",
      "1    463\n",
      "2    247\n",
      "3     21\n",
      "Name: weathersit, dtype: int64\n",
      "\n",
      "workingday属性的不同取值和出现的次数\n",
      "1    500\n",
      "0    231\n",
      "Name: workingday, dtype: int64\n",
      "\n",
      "mnth属性的不同取值和出现的次数\n",
      "12    62\n",
      "10    62\n",
      "8     62\n",
      "7     62\n",
      "5     62\n",
      "3     62\n",
      "1     62\n",
      "11    60\n",
      "9     60\n",
      "6     60\n",
      "4     60\n",
      "2     57\n",
      "Name: mnth, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "categorical_features = [ 'weathersit','workingday','mnth',]\n",
    "for col in categorical_features:\n",
    "        print ('\\n%s属性的不同取值和出现的次数'%col)\n",
    "        print (train[col].value_counts())\n",
    "        train[col] = train[col].astype('object')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>weathersit_1</th>\n",
       "      <th>weathersit_2</th>\n",
       "      <th>weathersit_3</th>\n",
       "      <th>workingday_0</th>\n",
       "      <th>workingday_1</th>\n",
       "      <th>mnth_1</th>\n",
       "      <th>mnth_2</th>\n",
       "      <th>mnth_3</th>\n",
       "      <th>mnth_4</th>\n",
       "      <th>mnth_5</th>\n",
       "      <th>mnth_6</th>\n",
       "      <th>mnth_7</th>\n",
       "      <th>mnth_8</th>\n",
       "      <th>mnth_9</th>\n",
       "      <th>mnth_10</th>\n",
       "      <th>mnth_11</th>\n",
       "      <th>mnth_12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   weathersit_1  weathersit_2  weathersit_3  workingday_0  workingday_1  \\\n",
       "0             0             1             0             1             0   \n",
       "1             0             1             0             1             0   \n",
       "2             1             0             0             0             1   \n",
       "3             1             0             0             0             1   \n",
       "4             1             0             0             0             1   \n",
       "\n",
       "   mnth_1  mnth_2  mnth_3  mnth_4  mnth_5  mnth_6  mnth_7  mnth_8  mnth_9  \\\n",
       "0       1       0       0       0       0       0       0       0       0   \n",
       "1       1       0       0       0       0       0       0       0       0   \n",
       "2       1       0       0       0       0       0       0       0       0   \n",
       "3       1       0       0       0       0       0       0       0       0   \n",
       "4       1       0       0       0       0       0       0       0       0   \n",
       "\n",
       "   mnth_10  mnth_11  mnth_12  \n",
       "0        0        0        0  \n",
       "1        0        0        0  \n",
       "2        0        0        0  \n",
       "3        0        0        0  \n",
       "4        0        0        0  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train_cat = train[categorical_features]\n",
    "x_train_cat = pd.get_dummies(x_train_cat)\n",
    "x_train_cat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.355170</td>\n",
       "      <td>0.373517</td>\n",
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       "      <td>0.284606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.379232</td>\n",
       "      <td>0.360541</td>\n",
       "      <td>0.715771</td>\n",
       "      <td>0.466215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.171000</td>\n",
       "      <td>0.144830</td>\n",
       "      <td>0.449638</td>\n",
       "      <td>0.465740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.175530</td>\n",
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       "      <td>0.607131</td>\n",
       "      <td>0.284297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.209120</td>\n",
       "      <td>0.197158</td>\n",
       "      <td>0.449313</td>\n",
       "      <td>0.339143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       temp     atemp       hum  windspeed\n",
       "0  0.355170  0.373517  0.828620   0.284606\n",
       "1  0.379232  0.360541  0.715771   0.466215\n",
       "2  0.171000  0.144830  0.449638   0.465740\n",
       "3  0.175530  0.174649  0.607131   0.284297\n",
       "4  0.209120  0.197158  0.449313   0.339143"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "mn_x = MinMaxScaler()\n",
    "numerical_features = ['temp','atemp','hum','windspeed']\n",
    "temp = mn_x.fit_transform(train[numerical_features])\n",
    "\n",
    "\n",
    "x_train_num = pd.DataFrame(data=temp,columns=numerical_features,index = train.index)\n",
    "x_train_num.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>weathersit_1</th>\n",
       "      <th>weathersit_2</th>\n",
       "      <th>weathersit_3</th>\n",
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       "      <th>mnth_10</th>\n",
       "      <th>mnth_11</th>\n",
       "      <th>mnth_12</th>\n",
       "      <th>temp</th>\n",
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       "      <th>windspeed</th>\n",
       "      <th>holiday</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.171000</td>\n",
       "      <td>0.144830</td>\n",
       "      <td>0.449638</td>\n",
       "      <td>0.465740</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.175530</td>\n",
       "      <td>0.174649</td>\n",
       "      <td>0.607131</td>\n",
       "      <td>0.284297</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0.209120</td>\n",
       "      <td>0.197158</td>\n",
       "      <td>0.449313</td>\n",
       "      <td>0.339143</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   weathersit_1  weathersit_2  weathersit_3  workingday_0  workingday_1  \\\n",
       "0             0             1             0             1             0   \n",
       "1             0             1             0             1             0   \n",
       "2             1             0             0             0             1   \n",
       "3             1             0             0             0             1   \n",
       "4             1             0             0             0             1   \n",
       "\n",
       "   mnth_1  mnth_2  mnth_3  mnth_4  mnth_5   ...     mnth_8  mnth_9  mnth_10  \\\n",
       "0       1       0       0       0       0   ...          0       0        0   \n",
       "1       1       0       0       0       0   ...          0       0        0   \n",
       "2       1       0       0       0       0   ...          0       0        0   \n",
       "3       1       0       0       0       0   ...          0       0        0   \n",
       "4       1       0       0       0       0   ...          0       0        0   \n",
       "\n",
       "   mnth_11  mnth_12      temp     atemp       hum  windspeed  holiday  \n",
       "0        0        0  0.355170  0.373517  0.828620   0.284606        0  \n",
       "1        0        0  0.379232  0.360541  0.715771   0.466215        0  \n",
       "2        0        0  0.171000  0.144830  0.449638   0.465740        0  \n",
       "3        0        0  0.175530  0.174649  0.607131   0.284297        0  \n",
       "4        0        0  0.209120  0.197158  0.449313   0.339143        0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train = pd.concat([x_train_cat,x_train_num,train['holiday']], axis = 1, ignore_index = False)\n",
    "x_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>mnth_12</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>holiday</th>\n",
       "      <th>yr</th>\n",
       "      <th>cnt</th>\n",
       "      <th>weekday</th>\n",
       "      <th>season</th>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.379232</td>\n",
       "      <td>0.360541</td>\n",
       "      <td>0.715771</td>\n",
       "      <td>0.466215</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>801</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.171000</td>\n",
       "      <td>0.144830</td>\n",
       "      <td>0.449638</td>\n",
       "      <td>0.465740</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1349</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.175530</td>\n",
       "      <td>0.174649</td>\n",
       "      <td>0.607131</td>\n",
       "      <td>0.284297</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1562</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.209120</td>\n",
       "      <td>0.197158</td>\n",
       "      <td>0.449313</td>\n",
       "      <td>0.339143</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1600</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant  weathersit_1  weathersit_2  weathersit_3  workingday_0  \\\n",
       "0        1             0             1             0             1   \n",
       "1        2             0             1             0             1   \n",
       "2        3             1             0             0             0   \n",
       "3        4             1             0             0             0   \n",
       "4        5             1             0             0             0   \n",
       "\n",
       "   workingday_1  mnth_1  mnth_2  mnth_3  mnth_4   ...    mnth_12      temp  \\\n",
       "0             0       1       0       0       0   ...          0  0.355170   \n",
       "1             0       1       0       0       0   ...          0  0.379232   \n",
       "2             1       1       0       0       0   ...          0  0.171000   \n",
       "3             1       1       0       0       0   ...          0  0.175530   \n",
       "4             1       1       0       0       0   ...          0  0.209120   \n",
       "\n",
       "      atemp       hum  windspeed  holiday  yr   cnt  weekday  season  \n",
       "0  0.373517  0.828620   0.284606        0   0   985        6       1  \n",
       "1  0.360541  0.715771   0.466215        0   0   801        0       1  \n",
       "2  0.144830  0.449638   0.465740        0   0  1349        1       1  \n",
       "3  0.174649  0.607131   0.284297        0   0  1562        2       1  \n",
       "4  0.197158  0.449313   0.339143        0   0  1600        3       1  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FE_train = pd.concat([train['instant'],x_train, train['yr'],train['cnt'],train['weekday'],train['season']], axis = 1)\n",
    "FE_train.to_csv('FE_day.csv',index=False)\n",
    "FE_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 27 columns):\n",
      "instant         731 non-null int64\n",
      "weathersit_1    731 non-null uint8\n",
      "weathersit_2    731 non-null uint8\n",
      "weathersit_3    731 non-null uint8\n",
      "workingday_0    731 non-null uint8\n",
      "workingday_1    731 non-null uint8\n",
      "mnth_1          731 non-null uint8\n",
      "mnth_2          731 non-null uint8\n",
      "mnth_3          731 non-null uint8\n",
      "mnth_4          731 non-null uint8\n",
      "mnth_5          731 non-null uint8\n",
      "mnth_6          731 non-null uint8\n",
      "mnth_7          731 non-null uint8\n",
      "mnth_8          731 non-null uint8\n",
      "mnth_9          731 non-null uint8\n",
      "mnth_10         731 non-null uint8\n",
      "mnth_11         731 non-null uint8\n",
      "mnth_12         731 non-null uint8\n",
      "temp            731 non-null float64\n",
      "atemp           731 non-null float64\n",
      "hum             731 non-null float64\n",
      "windspeed       731 non-null float64\n",
      "holiday         731 non-null int64\n",
      "yr              731 non-null int64\n",
      "cnt             731 non-null int64\n",
      "weekday         731 non-null object\n",
      "season          731 non-null object\n",
      "dtypes: float64(4), int64(4), object(2), uint8(17)\n",
      "memory usage: 69.3+ KB\n"
     ]
    }
   ],
   "source": [
    "FE_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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